Data augmentation for deep-learning-based electroencephalography

E Lashgari, D Liang, U Maoz - Journal of Neuroscience Methods, 2020‏ - Elsevier
Background Data augmentation (DA) has recently been demonstrated to achieve
considerable performance gains for deep learning (DL)—increased accuracy and stability …

Sleep in Alzheimer's disease: a systematic review and meta-analysis of polysomnographic findings

Y Zhang, R Ren, L Yang, H Zhang, Y Shi… - Translational …, 2022‏ - nature.com
Polysomnography (PSG) studies of sleep changes in Alzheimer's disease (AD) have
reported but not fully established the relationship between sleep disturbances and AD. To …

Insomnia in the elderly: a review

D Patel, J Steinberg, P Patel - Journal of Clinical Sleep Medicine, 2018‏ - jcsm.aasm.org
Background: Insomnia remains one of the most common sleep disorders encountered in the
geriatric clinic population, frequently characterized by the subjective complaint of difficulty …

Short-and long-term health consequences of sleep disruption

G Medic, M Wille, MEH Hemels - Nature and science of sleep, 2017‏ - Taylor & Francis
Sleep plays a vital role in brain function and systemic physiology across many body
systems. Problems with sleep are widely prevalent and include deficits in quantity and …

Evaluating reliability in wearable devices for sleep staging

V Birrer, M Elgendi, O Lambercy, C Menon - NPJ Digital Medicine, 2024‏ - nature.com
Sleep is crucial for physical and mental health, but traditional sleep quality assessment
methods have limitations. This sco** review analyzes 35 articles from the past decade …

Uncovering the structure of clinical EEG signals with self-supervised learning

H Banville, O Chehab, A Hyvärinen… - Journal of Neural …, 2021‏ - iopscience.iop.org
Objective. Supervised learning paradigms are often limited by the amount of labeled data
that is available. This phenomenon is particularly problematic in clinically-relevant data …

Normal polysomnography parameters in healthy adults: a systematic review and meta-analysis

MI Boulos, T Jairam, T Kendzerska, J Im… - The Lancet …, 2019‏ - thelancet.com
Background Existing normal polysomnography values are not truly normative as they are
based on small sample sizes due to the fact that polysomnography is expensive and …

A deep transfer learning approach for wearable sleep stage classification with photoplethysmography

M Radha, P Fonseca, A Moreau, M Ross, A Cerny… - NPJ digital …, 2021‏ - nature.com
Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography
(PPG) could open the way for better sleep disorder screening and health monitoring …

WiFi-sleep: Sleep stage monitoring using commodity Wi-Fi devices

B Yu, Y Wang, K Niu, Y Zeng, T Gu… - IEEE internet of …, 2021‏ - ieeexplore.ieee.org
Sleep monitoring is essential to people's health and wellbeing, which can also assist in the
diagnosis and treatment of sleep disorder. Compared with contact-based solutions …

Automatic sleep stage classification: From classical machine learning methods to deep learning

RN Sekkal, F Bereksi-Reguig… - … Signal Processing and …, 2022‏ - Elsevier
Background and objectives The classification of sleep stages is a preliminary exam that
contributes to the diagnosis of possible sleep disorders. However, it is a tedious and time …